"""
Machine learning module for Python
==================================

sklearn is a Python module integrating classical machine
learning algorithms in the tightly-knit world of scientific Python
packages (numpy, scipy, matplotlib).

It aims to provide simple and efficient solutions to learning problems
that are accessible to everybody and reusable in various contexts:
machine-learning as a versatile tool for science and engineering.

See http://scikit-learn.org for complete documentation.
"""
import sys
import logging
import os

from ._config import get_config, set_config, config_context

logger = logging.getLogger(__name__)
logger.addHandler(logging.StreamHandler())
logger.setLevel(logging.INFO)


# PEP0440 compatible formatted version, see:
# https://www.python.org/dev/peps/pep-0440/
#
# Generic release markers:
#   X.Y
#   X.Y.Z   # For bugfix releases
#
# Admissible pre-release markers:
#   X.YaN   # Alpha release
#   X.YbN   # Beta release
#   X.YrcN  # Release Candidate
#   X.Y     # Final release
#
# Dev branch marker is: 'X.Y.dev' or 'X.Y.devN' where N is an integer.
# 'X.Y.dev0' is the canonical version of 'X.Y.dev'
#
__version__ = '0.23.dev0'


# On OSX, we can get a runtime error due to multiple OpenMP libraries loaded
# simultaneously. This can happen for instance when calling BLAS inside a
# prange. Setting the following environment variable allows multiple OpenMP
# libraries to be loaded. It should not degrade performances since we manually
# take care of potential over-subcription performance issues, in sections of
# the code where nested OpenMP loops can happen, by dynamically reconfiguring
# the inner OpenMP runtime to temporarily disable it while under the scope of
# the outer OpenMP parallel section.
os.environ.setdefault("KMP_DUPLICATE_LIB_OK", "True")

# Workaround issue discovered in intel-openmp 2019.5:
# https://github.com/ContinuumIO/anaconda-issues/issues/11294
os.environ.setdefault("KMP_INIT_AT_FORK", "FALSE")

try:
    # This variable is injected in the __builtins__ by the build
    # process. It is used to enable importing subpackages of sklearn when
    # the binaries are not built
    # mypy error: Cannot determine type of '__SKLEARN_SETUP__'
    __SKLEARN_SETUP__  # type: ignore
except NameError:
    __SKLEARN_SETUP__ = False

if __SKLEARN_SETUP__:
    sys.stderr.write('Partial import of sklearn during the build process.\n')
    # We are not importing the rest of scikit-learn during the build
    # process, as it may not be compiled yet
else:
    # `_distributor_init` allows distributors to run custom init code.
    # For instance, for the Windows wheel, this is used to pre-load the
    # vcomp shared library runtime for OpenMP embedded in the sklearn/.libs
    # sub-folder.
    # It is necessary to do this prior to importing show_versions as the
    # later is linked to the OpenMP runtime to make it possible to introspect
    # it and importing it first would fail if the OpenMP dll cannot be found.
    from . import _distributor_init  # noqa: F401
    from . import __check_build  # noqa: F401
    from .base import clone
    from .utils._show_versions import show_versions

    __all__ = ['calibration', 'cluster', 'covariance', 'cross_decomposition',
               'datasets', 'decomposition', 'dummy', 'ensemble', 'exceptions',
               'experimental', 'externals', 'feature_extraction',
               'feature_selection', 'gaussian_process', 'inspection',
               'isotonic', 'kernel_approximation', 'kernel_ridge',
               'linear_model', 'manifold', 'metrics', 'mixture',
               'model_selection', 'multiclass', 'multioutput',
               'naive_bayes', 'neighbors', 'neural_network', 'pipeline',
               'preprocessing', 'random_projection', 'semi_supervised',
               'svm', 'tree', 'discriminant_analysis', 'impute', 'compose',
               # Non-modules:
               'clone', 'get_config', 'set_config', 'config_context',
               'show_versions']


def setup_module(module):
    """Fixture for the tests to assure globally controllable seeding of RNGs"""
    import os
    import numpy as np
    import random

    # Check if a random seed exists in the environment, if not create one.
    _random_seed = os.environ.get('SKLEARN_SEED', None)
    if _random_seed is None:
        _random_seed = np.random.uniform() * np.iinfo(np.int32).max
    _random_seed = int(_random_seed)
    print("I: Seeding RNGs with %r" % _random_seed)
    np.random.seed(_random_seed)
    random.seed(_random_seed)
